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Hierarchical Fusion Network for Multimodal Sentiment Analysis

Hierarchical Fusion Network for Multimodal Sentiment Analysi...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Yuyao Hua Ruirui Ji Sifan Yang Yi Geng Wei Gao Yun Tan School of Automation and Information Engineering Xi'an University of Technology Xi'an China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

In the multimodal sentiment analysis task, the role of text is superior to that of non-text modalities. Effectively learning the interaction relationships between modalities to support the text modality while preserving the specific information inherent to each modality to obtain a fusion representation that includes richer sentiment information is a challenging task. To address this issue, a multimodal sentiment analysis model based on the Hierarchical Fusion Network is proposed. the model is text-dominated and employs feature fusion to capture the interaction between modalities. Additionally, it preserves the unique information within each modality through decision fusion. the sentiment information from audio and visual modalities is processed using Gated Linear Unit, which improves the accuracy of sentiment analysis. Experimental results on the CMU-MOSI and CMU-MOSEI datasets demonstrate the effectiveness of the proposed HFN model, outperforming previous approaches in multimodal sentiment analysis.

关键词： Sentiment analysis Analytical models Visualization data preprocessing Transforms machine learning Logic gates Feature extraction Reliability data mining

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Evaluation of machine learning Performance Based on BERT data Representation with LSTM Model to Conduct Sentiment Analysis in Indonesian for Predicting Voices of Social Media Users in the 2024 Indonesia Presidential Election 5

Evaluation of Machine Learning Performance Based on BERT Dat...

引用

5th international conference on Information and Communications Technology, ICOIACT 2022

作者： Mandhasiya, Dwi Guna Murfi, Hendri Bustamam, Alhadi Anki, Prasnurzaki Universitas Indonesia Department of Mathematics Depok Indonesia

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

In this era, the innovation of science and technology has changed rapidly, such as Artificial Intelligence (A.I.) which has helped a lot in human life. Deep learning (DL) as part of A.I. is the development of one of the machine learning models, namely the neural network. With many neural network layers, deep learning models can perform feature extraction and classification processes in a single architecture. this model has proven to perform state-of-the-art machine learning techniques in text classification, pattern recognition, speech, and imagery. Various text classification tasks, including sentiment analysis, have gone beyond AI-based approaches in Deep learning models. Text data can come from multiple sources, including social media, such as comments on videos on YouTube. Sentiment analysis, one of the opinion mining, is a computational study that analyzes people's opinions from texts. In this research, machine learning performance analysis is carried out on a deep learning method based on BERT data representation with the LSTM method. the implementation of the model uses youtube commentary data on political videos related to the 2024 presidential election in Indonesia;performance analysis is carried out using accuracy, precision, and recall metrics. In this study, the accuracy of the BERT-LSTM model outperformed the BERT model with an accuracy of 0.8783. © 2022 IEEE.

关键词： Sentiment analysis

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Federated learning paradigm in E-health systems: An overview 4

Federated Learning paradigm in E-health systems: An overview

引用

4th international conference on pattern Analysis and Intelligent Systems, PAIS 2022

作者： Sellamna, Abdellatif Boukhamla, Akram Zine Eddine Benkaddour, Mohammed Kamel University of Kasdi Merbah Laboratoire d'Intelligence Artificielle et des Technologies de l'Information Ouargla Algeria

ISBN: (数字)9781665461610

ISBN: (纸本)9781665461610

Every day, professionals generate and use massive healthcare data to save, treat and ameliorate the lives of patients. the healthcare industry has adopted cloud-based solutions to solve several problems in a cost-effective manner. therefore, privacy and security mechanisms should be deployed to protect valuable medical information from unauthorized access. Much of the work in literature in recent years has focused on using artificial intelligence techniques such as deep learning and federated learning to solve various problems in the health field. Federated learning (FL) is a special technique for machine learning for privacy preservation. this study aims to compare the traditional centralized training approach and FL to show the advantages of using FL in the medical field and prove that FL can be adopted for security and data latency in e-health systems. the results obtained showed the feasibility of FL when compared to traditional methods used in the aspect of securing data and latency in the medical field. © 2022 IEEE.

关键词： Cost effectiveness

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Significant CC400 Functional Brain Parcellations Based LeNet5 Convolutional Neural Network for Autism Spectrum Disorder Detection 5th

Significant CC400 Functional Brain Parcellations Based LeNet...

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5th international conference on Recent Trends in Image Processing and pattern recognition (RTIP2R)

作者： Othmani, Alice Bizet, thibaut Pellerin, Tanguy Hamdi, Badr Bock, Marc-Antoine Dev, Soumyabrata Univ Paris Est LISSI UPEC F-94400 Vitry Sur Seine France EFREI Paris Engn Sch F-94800 Villejuif France Univ Coll Dublin Sch Comp Sci Dublin Ireland

ISBN: (纸本)9783031235986;9783031235993

machine learning and computer vision have opened new pathways to investigate imaging data captured from different sensors. Numerous application areas are getting benefit from these advancements and one of these areas is medical imaging. Despite rapid advancements in machine learning based medical condition diagnosis systems (CADs), some ailments and disorders are hard to diagnose/classify due to the absence or the lack of consensus on biomarkers for specific disorders, like the Autism Spectrum Disorder (ASD). In this study, the challenging problem of classification of ASD using the magnetic resonance imaging (MRI) data is tackled. Hence, we propose an interpretable deep neural network based approach for ASD detection from MRI images. Our proposed explanation method is based on the selection of four regions of interest from the MRI images. the four significant CC400 functional brain parcellations are then concatenated and fed to a LeNet-5-based convolutional neural network to predict ASD. the performances of the proposed approach are evaluated on ABIDE dataset and promising results are achieved. three augmented datasets are considered and an accuracy of 95% is achieved by using LeNet-5 which outperforms VGG16 and ResNet-50. the achieved accuracy outperforms also the existing deep neural networks based approaches on ABIDE dataset. the use of the four significant CC400 functional brain parcellations makes our approach more interpretable and more accurate.

关键词： Deep learning Convolutional Neural Network Autism Spectrum Disorder ABIDE I MRI images

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Construction of an Intelligent Identification System for Concrete Bridge Cracks by Integrating machine learning and Neural Network Models 4

Construction of an Intelligent Identification System for Con...

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4th international conference on Industrial IoT, Big data and Supply Chain, IIoTBDSC 2023

作者： Zhang, Zanpeng Xu, Meng Luo, Lingyu Hong, Zhong China Merchants Chongqing Communications Technology Research and Design Institute Co. Ltd. Chongqing400067 China China Merchants Chongqing Highway Engineering Testing Center Co. Ltd. Chongqing400067 China Chongqing Jiangbei District Heji Primary School Chongqing400000 China Tibet Autonomous Region Key Highway Construction Project Management Center Lhasa850000 China

ISBN: (纸本)9798350341690

With the increasing use time and frequency of bridges, they will inevitably be subjected to a large number of traffic vehicles and various human damage. In addition, the severe erosion of the natural environment will cause different situations and degrees of damage to the surface of the beam. According to relevant data, cracks are the main factor causing damage to concrete bridges. therefore, this paper combines machine learning and NN (Neural Network) model to build an intelligent identification system for concrete bridge cracks. through the system, we can clearly see the recognition of the model on the bridge crack picture. the numerical representation on the diagonal of the Confusion matrix identifies the correct type, and the numerical representation not on the diagonal represents the wrong type. And the research results show that the classification accuracy of this algorithm for the verification set is high, close to the training set, and the Loss function is close to the Loss function of the training set on the basis of the overall fluctuation, so there is no over fitting phenomenon, the uncomfortable training results are good, and the training time is very similar. the algorithm proposed in this paper can accurately identify concrete surface crack images in a short period of time, indicating that the algorithm can be used for crack localization. © 2023 IEEE.

关键词： Neural network models

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data analysis of Global Perceived Stress Scores during Covid-19 4

Data analysis of Global Perceived Stress Scores during Covid...

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4th international conference on Circuits, Control, Communication and Computing, I4C 2022

作者： Muttaraju, Chakita Kabber, Anusha Parimala, S. Anagani, Prajwal Srinivasa, Gowri PES University Department of Computer Science and Engineering Bengaluru India PES University PES Center for Pattern Recognition Dept. of CSE Bengaluru India

ISBN: (纸本)9798350397475

the Covid-19 Pandemic that broke out in late December 2019 has had a widespread negative effect on the mental health of people around the world. this work aims to elicit features that had a major influence on mental health during the pandemic to better understand preventive measures and remedial actions that can be taken to help individuals in need. Along with factors such as demographic age, gender, marital status, and employment status, additional information such as the effect of media used as a source of information, coping methods, trust in the country's government, and healthcare organizations was analyzed to find their correlation (if any) to the perceived stress of the individual. machine learning techniques such as XGBoost, AdaBoost, Decision Trees, Ordinal regression, k-Nearest Neighbors, Lasso and Ridge regression were used to arrive at a relationship between the perceived stress scores and the features considered. On interpreting results from the different models, we conclude that the main factor influencing stress scores was loneliness followed by features indicating trust in government, compliance with Covid-19 preventive measures and concerns regarding the pandemic. © 2022 IEEE.

关键词： Decision trees

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machine learning Improved Chan-Taylor Co-Weighted Localization Algorithm 4

Machine Learning Improved Chan-Taylor Co-Weighted Localizati...

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4th international conference on Frontiers Technology of Information and Computer, ICFTIC 2022

作者： Tong, QingSheng Zhao, Shuang Wang, DaYu Yang, HongWei Changchun University of Science and Technology Electronics and Communication Engineering Jilin Province Changchun City China Changchun Public Security Bureau Jilin Province Changchun City China

ISBN: (纸本)9798350321951

With the rise of the Internet of things and smart cities, indoor positioning technology becomes more and more important. Among the existing network technologies, ultra-wide band (UWB) has the advantages of high transmission rate, and strong penetration ability. However, its signal propagation is susceptible to N on- Line-of-Sight (NLOS) effects. therefore, this paper focuses on indoor positioning in NLOS environments, and studies the Chan-Taylor co-weighted positioning algorithm improved by machine learning. the main research contents include: 1. A machine learning method is proposed to identify and classify NLOS signals. Using a sliding window to dynamically calculate the variance and mean of multiple groups of TDOA measurements in a short period of time as the input to the recognition algorithm, and classify them into three categories: Line-of-Sight (LOS), low NLOS error, and high NLOS error. 2. Based on the Chan- Taylor co-location algorithm, the algorithm introduces the concept of weighting coefficient, optimizes the observation data, and finally obtains the target position coordinates. the simulation results show that the positioning accuracy and system stability of the algorithm are improved compared with the Chan algorithm and the Chan-Taylor co-location algorithm. © 2022 IEEE.

关键词： machine learning

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Research on Scholars' Influence Based on data Integration Under the Academic Big data 4

Research on Scholars' Influence Based on Data Integration Un...

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4th international conference on machine learning, Big data and Business Intelligence, MLBDBI 2022

作者： Qi, Shan Li, Yajie Wang, Jie Lv, Yali Taiyuan Normal University School of Education Taiyuan China Bank of China Shanxi Branch Taiyuan China Shanxi University of Finance and Economics School of Information Taiyuan China

ISBN: (纸本)9798350333947

[Purpose/Significance] Evaluating the scholars' influence from large-scale academic data has been a hot task in data mining. At present, there is a lack of methods to integrate attribute information and network topology information to evaluate the influence of scholars based on representative papers. From the perspective of data independence, attribute information and network topology information are complementary. [Methods/Process] To overcome this deficiency, this study proposes a new method to evaluate the influence of scholars considering both attribute information of published papers and network topology information of co-authors. First, author's single paper influence is calculated according to the attribute information of the paper, including the author's signature order, the journal impact factor and the citation frequency. then a weighted co-authorship network is built based on the author's single paper influence. Finally, a method integrating various node centrality measures is poposed to evaluate the influence of scholars. [Results/Conclusion] the experiment results demonstrate that the proposed method is effective for evaluation of scholars' Influence. If a scholar has more representative papers with high influence and is at the center of the cooperation network, the scholar's academic influence is greater than that of others. © 2022 IEEE.

关键词： Network topology

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IoT and machine learning-Based Covid-19 Healthcare Monitoring System Using Face recognition 4th

IoT and Machine Learning-Based Covid-19 Healthcare Monitorin...

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4th international conference on machine learning, Image Processing, Network Security and data Sciences (MIND)

作者： Vaswani, Chahat Chimaniya, Shalini Ranjan, Rajnish K. Bhawsar, Yachana Govt Womens Polytechn Coll Bhopal 462016 India LNCT Univ Bhopal 462042 India

ISBN: (纸本)9783031243660;9783031243677

After facing the horrible COVID-19 pandemic, steadily life is getting back to normal again. this pandemic came with opportunities as well, especially for researchers to come out with novel ideas and handle the situation. Many researchers have contributed with their dedicated research work with the help of recent technology to overcome similar circumstances. this paper presents a novel idea for proper monitoring and detecting normal/abnormal health using AI-based models. Proper monitoring and detection of symptoms are essential to ensure the health of members. this model is devised using several IoTs components and various ML (machine learning) techniques have been used to get comparative enhanced results. the hardware used Raspberry Pi 4 model B, which is the main hardware connected to several sensors like MLX906014 non-contact thermal sensor and MAX30100 pulse oximeter and heart rate sensor to measure body temperature without contact and to calculate the level of oxygen in the blood and measuring pulse rate respectively. Additionally, a Camera module for facilitating face recognition features for devices has been used. AnAlert will be sent to Admin if someone has an abnormal temperature and oxygen level. the Firebase database is used to store information and it gets updated in real-time. People's health history can be further analyzed through graphs for visualization and monitored by the administrator.

关键词： machine learning Internet of things Sensors Support Vector machine Face recognition

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machine learning Model for Medical data Classification for Accurate Brain Tumor Cell Detection 4th

Machine Learning Model for Medical Data Classification for A...

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4th international conference on Innovative Product Design and Intelligent Manufacturing System, IPDIMS 2022

作者： Navabothu, Gnana Sri Sai Sujith Sakode, Himanshu Gottipati, Jagathi Rama Devi, Polagani Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada520007 India

ISBN: (纸本)9789819916641

the health industry has grown into a large organization, and a vast number of medical data can be produced every day by the healthcare industry to collect information for the prediction of future diseases that can be produced by a patient using treatment history and health information. In a variety of medical diagnostic applications, automated defect identification in medical imaging has become an arising sector. Automated brain tumor identification and categorization are the focus of our work. In most cases, CT or MRI scans are used to examine the anatomy of the brain. the main objective of brain tumor detection is to help in clinical diagnosis. the goal is to design a machine learning method that ensures the existence of a tumor and to provide a reliable technique of tumor diagnosis that aids medical staff to provide proper medication to the patient. We get deep convolutional neural network (CNN) models that have already been trained using the transfer learning method to extract features from brain MRI scans. the Visual Geometry Group (VGG 16) is the most notable model when it comes to image recognition architectures as it surpasses and outperforms other traditional convolutional neural network models. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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